Brain MRI Segmentation Using Optimized BCFCM Base on GA and PSO

Segmentation of medical images is one of the initial pre-processing for designing automated diagnosing systems. Magnetic Resonance Imaging (MRI) of the brain is associated with intensity uncertainty due to destructive artificial factors in the imaging process such as noise and Intensity Non-Uniformity (INU). As a result, segmentation of these images is a challenging issue. Due to uncertainty in brain MRI, researchers have employed fuzzy methods for segmenting brain MRI. BCFCM is one of the fuzzy segmentation methods in which information of neighboring pixels are also used for segmentation. This method has different parameters which inappropriate selection of that, greatly reduces the performance of the method. In this paper an optimized BCFCM with two structure is proposed for brain MRI segmentation. In the optimization process, GA and PSO are used. Appropriate performance of the proposed method is demonstrated by simulation results of standard Brain-Web datasets using Tanimoto and Dice similarity measures.